Overview

Dataset statistics

Number of variables20
Number of observations100968
Missing cells0
Missing cells (%)0.0%
Duplicate rows13793
Duplicate rows (%)13.7%
Total size in memory15.4 MiB
Average record size in memory160.0 B

Variable types

NUM15
CAT5

Reproduction

Analysis started2020-08-25 01:22:09.087976
Analysis finished2020-08-25 01:22:57.242711
Duration48.15 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 13793 (13.7%) duplicate rows Duplicates
DRUG_TEST_TYPE_(3_of_3) is highly correlated with DRUG_TEST_RESULTS_(3_of_3)High correlation
DRUG_TEST_RESULTS_(3_of_3) is highly correlated with DRUG_TEST_TYPE_(3_of_3)High correlation
DRUG_TEST_RESULTS_(3_of_3) has 90394 (89.5%) zeros Zeros
EJECTION_PATH has 87477 (86.6%) zeros Zeros
DRUG_TEST_TYPE_(3_of_3) has 1068 (1.1%) zeros Zeros
CASE_STATE has 2271 (2.2%) zeros Zeros
DRUG_TEST_TYPE has 16153 (16.0%) zeros Zeros

Variables

RELATED_FACTOR_(3)-PERSON_LEVEL
Real number (ℝ≥0)

Distinct count33
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.00750732905475
Minimum0
Maximum32
Zeros3
Zeros (%)< 0.1%
Memory size788.9 KiB
2020-08-25T01:22:57.288697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q119
median19
Q319
95-th percentile19
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8078197301
Coefficient of variation (CV)0.04250003517
Kurtosis201.9375804
Mean19.00750733
Median Absolute Deviation (MAD)0
Skewness0.9361049922
Sum1919150
Variance0.6525727163
2020-08-25T01:22:57.402392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1910036299.4%
 
302650.3%
 
81280.1%
 
734< 0.1%
 
2030< 0.1%
 
1224< 0.1%
 
1422< 0.1%
 
3120< 0.1%
 
1111< 0.1%
 
297< 0.1%
 
226< 0.1%
 
35< 0.1%
 
65< 0.1%
 
245< 0.1%
 
154< 0.1%
 
264< 0.1%
 
24< 0.1%
 
183< 0.1%
 
213< 0.1%
 
253< 0.1%
 
93< 0.1%
 
13< 0.1%
 
03< 0.1%
 
42< 0.1%
 
172< 0.1%
 
Other values (8)10< 0.1%
 
ValueCountFrequency (%) 
03< 0.1%
 
13< 0.1%
 
24< 0.1%
 
35< 0.1%
 
42< 0.1%
 
51< 0.1%
 
65< 0.1%
 
734< 0.1%
 
81280.1%
 
93< 0.1%
 
ValueCountFrequency (%) 
321< 0.1%
 
3120< 0.1%
 
302650.3%
 
297< 0.1%
 
281< 0.1%
 
271< 0.1%
 
264< 0.1%
 
253< 0.1%
 
245< 0.1%
 
232< 0.1%
 

METHOD_OF_DRUG_DETERMINATION
Real number (ℝ≥0)

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.980082798510419
Minimum0
Maximum4
Zeros621
Zeros (%)0.6%
Memory size788.9 KiB
2020-08-25T01:22:57.822402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q13
median3
Q33
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3367975239
Coefficient of variation (CV)0.1130161632
Kurtosis40.28719788
Mean2.980082799
Median Absolute Deviation (MAD)0
Skewness-4.389624933
Sum300893
Variance0.1134325721
2020-08-25T01:22:57.933395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
39479493.9%
 
427612.7%
 
226752.6%
 
06210.6%
 
11170.1%
 
ValueCountFrequency (%) 
06210.6%
 
11170.1%
 
226752.6%
 
39479493.9%
 
427612.7%
 
ValueCountFrequency (%) 
427612.7%
 
39479493.9%
 
226752.6%
 
11170.1%
 
06210.6%
 

METHOD_ALCOHOL_DETERMINATION
Real number (ℝ≥0)

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0442120275730926
Minimum0
Maximum6
Zeros359
Zeros (%)0.4%
Memory size788.9 KiB
2020-08-25T01:22:58.060296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5662034135
Coefficient of variation (CV)0.2769788094
Kurtosis17.9583979
Mean2.044212028
Median Absolute Deviation (MAD)0
Skewness2.689822369
Sum206400
Variance0.3205863055
2020-08-25T01:22:58.181161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
28420883.4%
 
173357.3%
 
370827.0%
 
412381.2%
 
67210.7%
 
03590.4%
 
525< 0.1%
 
ValueCountFrequency (%) 
03590.4%
 
173357.3%
 
28420883.4%
 
370827.0%
 
412381.2%
 
525< 0.1%
 
67210.7%
 
ValueCountFrequency (%) 
67210.7%
 
525< 0.1%
 
412381.2%
 
370827.0%
 
28420883.4%
 
173357.3%
 
03590.4%
 

DRUG_TEST_RESULTS_(3_of_3)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count59
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.44155574043262
Minimum0
Maximum999
Zeros90394
Zeros (%)89.5%
Memory size788.9 KiB
2020-08-25T01:22:58.303645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation292.1212775
Coefficient of variation (CV)3.060734658
Kurtosis5.608476994
Mean95.44155574
Median Absolute Deviation (MAD)0
Skewness2.753366631
Sum9636543
Variance85334.84076
2020-08-25T01:22:58.405056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
09039489.5%
 
99992489.2%
 
17030.7%
 
9961890.2%
 
69545< 0.1%
 
60639< 0.1%
 
41735< 0.1%
 
35128< 0.1%
 
40725< 0.1%
 
41024< 0.1%
 
32121< 0.1%
 
60319< 0.1%
 
40118< 0.1%
 
99818< 0.1%
 
40214< 0.1%
 
60012< 0.1%
 
60511< 0.1%
 
99710< 0.1%
 
34310< 0.1%
 
1878< 0.1%
 
3768< 0.1%
 
1778< 0.1%
 
1557< 0.1%
 
3046< 0.1%
 
5135< 0.1%
 
Other values (34)630.1%
 
ValueCountFrequency (%) 
09039489.5%
 
17030.7%
 
1001< 0.1%
 
1362< 0.1%
 
1451< 0.1%
 
1557< 0.1%
 
1561< 0.1%
 
1571< 0.1%
 
1653< 0.1%
 
1673< 0.1%
 
ValueCountFrequency (%) 
99992489.2%
 
99818< 0.1%
 
99710< 0.1%
 
9961890.2%
 
9241< 0.1%
 
7951< 0.1%
 
7024< 0.1%
 
69545< 0.1%
 
60639< 0.1%
 
60511< 0.1%
 

EJECTION_PATH
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0845713493384042
Minimum0
Maximum9
Zeros87477
Zeros (%)86.6%
Memory size788.9 KiB
2020-08-25T01:22:58.521520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.843937008
Coefficient of variation (CV)2.622176042
Kurtosis3.406828131
Mean1.084571349
Median Absolute Deviation (MAD)0
Skewness2.30298195
Sum109507
Variance8.087977708
2020-08-25T01:22:58.637467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
08747786.6%
 
9100079.9%
 
715221.5%
 
67010.7%
 
14070.4%
 
33270.3%
 
82580.3%
 
51680.2%
 
2560.1%
 
445< 0.1%
 
ValueCountFrequency (%) 
08747786.6%
 
14070.4%
 
2560.1%
 
33270.3%
 
445< 0.1%
 
51680.2%
 
67010.7%
 
715221.5%
 
82580.3%
 
9100079.9%
 
ValueCountFrequency (%) 
9100079.9%
 
82580.3%
 
715221.5%
 
67010.7%
 
51680.2%
 
445< 0.1%
 
33270.3%
 
2560.1%
 
14070.4%
 
08747786.6%
 

EXTRICATION
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size788.9 KiB
1
89933
0
 
9826
2
 
1209
ValueCountFrequency (%) 
18993389.1%
 
098269.7%
 
212091.2%
 
2020-08-25T01:22:59.184371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
18993389.1%
 
098269.7%
 
212091.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number100968100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
18993389.1%
 
098269.7%
 
212091.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common100968100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
18993389.1%
 
098269.7%
 
212091.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII100968100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
18993389.1%
 
098269.7%
 
212091.2%
 

DRUG_TEST_TYPE_(3_of_3)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.258101576737184
Minimum0
Maximum6
Zeros1068
Zeros (%)1.1%
Memory size788.9 KiB
2020-08-25T01:22:59.297369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9057076606
Coefficient of variation (CV)0.4010925239
Kurtosis5.450893448
Mean2.258101577
Median Absolute Deviation (MAD)0
Skewness2.437227105
Sum227996
Variance0.8203063665
2020-08-25T01:22:59.407413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
29039489.5%
 
592489.2%
 
010681.1%
 
61170.1%
 
1840.1%
 
346< 0.1%
 
411< 0.1%
 
ValueCountFrequency (%) 
010681.1%
 
1840.1%
 
29039489.5%
 
346< 0.1%
 
411< 0.1%
 
592489.2%
 
61170.1%
 
ValueCountFrequency (%) 
61170.1%
 
592489.2%
 
411< 0.1%
 
346< 0.1%
 
29039489.5%
 
1840.1%
 
010681.1%
 

CASE_STATE
Real number (ℝ≥0)

ZEROS

Distinct count51
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.4259765470248
Minimum0
Maximum50
Zeros2271
Zeros (%)2.2%
Memory size788.9 KiB
2020-08-25T01:22:59.521249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median22
Q338
95-th percentile46
Maximum50
Range50
Interquartile range (IQR)29

Descriptive statistics

Standard deviation15.22821164
Coefficient of variation (CV)0.6500566415
Kurtosis-1.416085416
Mean23.42597655
Median Absolute Deviation (MAD)13
Skewness0.07160162336
Sum2365274
Variance231.8984297
2020-08-25T01:22:59.629106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
41015110.1%
 
4392319.1%
 
977387.7%
 
1040304.0%
 
3237653.7%
 
3336073.6%
 
3835583.5%
 
1334073.4%
 
2232803.2%
 
3531443.1%
 
228752.8%
 
4228592.8%
 
2524952.5%
 
4023622.3%
 
022712.2%
 
1822492.2%
 
4621012.1%
 
1420892.1%
 
1719141.9%
 
3018351.8%
 
517661.7%
 
4916791.7%
 
2416291.6%
 
3615731.6%
 
4715521.5%
 
Other values (26)1780817.6%
 
ValueCountFrequency (%) 
022712.2%
 
12080.2%
 
228752.8%
 
314211.4%
 
41015110.1%
 
517661.7%
 
66960.7%
 
73370.3%
 
82000.2%
 
977387.7%
 
ValueCountFrequency (%) 
503920.4%
 
4916791.7%
 
488620.9%
 
4715521.5%
 
4621012.1%
 
451900.2%
 
447350.7%
 
4392319.1%
 
4228592.8%
 
414030.4%
 

RELATED_FACTOR_(2)-PERSON_LEVEL
Real number (ℝ≥0)

Distinct count48
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.935365660407257
Minimum0
Maximum47
Zeros1
Zeros (%)< 0.1%
Memory size788.9 KiB
2020-08-25T01:22:59.747909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q129
median29
Q329
95-th percentile29
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.006200823
Coefficient of variation (CV)0.069333868
Kurtosis76.42562404
Mean28.93536566
Median Absolute Deviation (MAD)0
Skewness-3.087831092
Sum2921546
Variance4.024841743
2020-08-25T01:22:59.846552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
299926998.3%
 
124190.4%
 
442650.3%
 
182650.3%
 
462560.3%
 
4880.1%
 
20880.1%
 
10600.1%
 
3048< 0.1%
 
3320< 0.1%
 
3620< 0.1%
 
716< 0.1%
 
4214< 0.1%
 
3413< 0.1%
 
2410< 0.1%
 
910< 0.1%
 
269< 0.1%
 
178< 0.1%
 
238< 0.1%
 
356< 0.1%
 
146< 0.1%
 
156< 0.1%
 
86< 0.1%
 
475< 0.1%
 
404< 0.1%
 
Other values (23)49< 0.1%
 
ValueCountFrequency (%) 
01< 0.1%
 
12< 0.1%
 
23< 0.1%
 
31< 0.1%
 
4880.1%
 
51< 0.1%
 
63< 0.1%
 
716< 0.1%
 
86< 0.1%
 
910< 0.1%
 
ValueCountFrequency (%) 
475< 0.1%
 
462560.3%
 
451< 0.1%
 
442650.3%
 
433< 0.1%
 
4214< 0.1%
 
411< 0.1%
 
404< 0.1%
 
393< 0.1%
 
383< 0.1%
 

ALCOHOL_TEST_TYPE
Real number (ℝ≥0)

Distinct count10
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.839265906029633
Minimum0
Maximum9
Zeros3
Zeros (%)< 0.1%
Memory size788.9 KiB
2020-08-25T01:22:59.954188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median4
Q39
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.329189455
Coefficient of variation (CV)0.398883951
Kurtosis-1.534252753
Mean5.839265906
Median Absolute Deviation (MAD)0
Skewness0.4883436165
Sum589579
Variance5.425123519
2020-08-25T01:23:00.063718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
45540554.9%
 
93355033.2%
 
698109.7%
 
214471.4%
 
52680.3%
 
72310.2%
 
81440.1%
 
1920.1%
 
318< 0.1%
 
03< 0.1%
 
ValueCountFrequency (%) 
03< 0.1%
 
1920.1%
 
214471.4%
 
318< 0.1%
 
45540554.9%
 
52680.3%
 
698109.7%
 
72310.2%
 
81440.1%
 
93355033.2%
 
ValueCountFrequency (%) 
93355033.2%
 
81440.1%
 
72310.2%
 
698109.7%
 
52680.3%
 
45540554.9%
 
318< 0.1%
 
214471.4%
 
1920.1%
 
03< 0.1%
 
Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size788.9 KiB
2
74725
1
18335
3
 
6282
0
 
1626
ValueCountFrequency (%) 
27472574.0%
 
11833518.2%
 
362826.2%
 
016261.6%
 
2020-08-25T01:23:00.648826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
27472574.0%
 
11833518.2%
 
362826.2%
 
016261.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number100968100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
27472574.0%
 
11833518.2%
 
362826.2%
 
016261.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common100968100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
27472574.0%
 
11833518.2%
 
362826.2%
 
016261.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII100968100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
27472574.0%
 
11833518.2%
 
362826.2%
 
016261.6%
 
Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size788.9 KiB
1
45448
0
34810
2
10799
3
9911
ValueCountFrequency (%) 
14544845.0%
 
03481034.5%
 
21079910.7%
 
399119.8%
 
2020-08-25T01:23:01.213026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
14544845.0%
 
03481034.5%
 
21079910.7%
 
399119.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number100968100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14544845.0%
 
03481034.5%
 
21079910.7%
 
399119.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common100968100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
14544845.0%
 
03481034.5%
 
21079910.7%
 
399119.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII100968100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
14544845.0%
 
03481034.5%
 
21079910.7%
 
399119.8%
 

DRUG_TEST_TYPE
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2418885191347755
Minimum0
Maximum6
Zeros16153
Zeros (%)16.0%
Memory size788.9 KiB
2020-08-25T01:23:01.328319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.539762086
Coefficient of variation (CV)0.6868147423
Kurtosis0.05216568456
Mean2.241888519
Median Absolute Deviation (MAD)0
Skewness0.6995243504
Sum226359
Variance2.37086728
2020-08-25T01:23:01.444526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
26407263.5%
 
01615316.0%
 
51580515.7%
 
619722.0%
 
114221.4%
 
413041.3%
 
32400.2%
 
ValueCountFrequency (%) 
01615316.0%
 
114221.4%
 
26407263.5%
 
32400.2%
 
413041.3%
 
51580515.7%
 
619722.0%
 
ValueCountFrequency (%) 
619722.0%
 
51580515.7%
 
413041.3%
 
32400.2%
 
26407263.5%
 
114221.4%
 
01615316.0%
 

AGE
Real number (ℝ≥0)

Distinct count99
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.10670707550907
Minimum0
Maximum99
Zeros446
Zeros (%)0.4%
Memory size788.9 KiB
2020-08-25T01:23:01.564197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q120
median32
Q349
95-th percentile81
Maximum99
Range99
Interquartile range (IQR)29

Descriptive statistics

Standard deviation22.10964127
Coefficient of variation (CV)0.5958394859
Kurtosis0.2693850882
Mean37.10670708
Median Absolute Deviation (MAD)13
Skewness0.8633521931
Sum3746590
Variance488.836237
2020-08-25T01:23:01.669208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1835943.6%
 
1934323.4%
 
2031403.1%
 
2131113.1%
 
1731003.1%
 
2226342.6%
 
9925522.5%
 
1624492.4%
 
2323292.3%
 
2421732.2%
 
2520422.0%
 
2617941.8%
 
2817681.8%
 
2717401.7%
 
3017221.7%
 
2916891.7%
 
3716641.6%
 
3916461.6%
 
3116391.6%
 
3816341.6%
 
4016151.6%
 
3516061.6%
 
4115871.6%
 
3615741.6%
 
4215701.6%
 
Other values (74)4716446.7%
 
ValueCountFrequency (%) 
04460.4%
 
16230.6%
 
26330.6%
 
35940.6%
 
46210.6%
 
55340.5%
 
65360.5%
 
75830.6%
 
85480.5%
 
95950.6%
 
ValueCountFrequency (%) 
9925522.5%
 
9723< 0.1%
 
9611< 0.1%
 
9521< 0.1%
 
9423< 0.1%
 
9330< 0.1%
 
92620.1%
 
91930.1%
 
90920.1%
 
891360.1%
 

SEATING_POSITION
Real number (ℝ≥0)

Distinct count26
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.990700023769907
Minimum0
Maximum25
Zeros7
Zeros (%)< 0.1%
Memory size788.9 KiB
2020-08-25T01:23:01.791425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q13
median3
Q36
95-th percentile16
Maximum25
Range25
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.794033728
Coefficient of variation (CV)0.8002459995
Kurtosis3.601701575
Mean5.990700024
Median Absolute Deviation (MAD)0
Skewness1.955037901
Sum604869
Variance22.98275939
2020-08-25T01:23:01.901789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
35733156.8%
 
61877918.6%
 
864426.4%
 
1652635.2%
 
1348574.8%
 
1421342.1%
 
2517681.8%
 
917341.7%
 
49280.9%
 
113410.3%
 
172190.2%
 
192030.2%
 
221980.2%
 
181500.1%
 
201400.1%
 
231050.1%
 
121000.1%
 
15750.1%
 
10680.1%
 
7580.1%
 
524< 0.1%
 
2420< 0.1%
 
2114< 0.1%
 
07< 0.1%
 
26< 0.1%
 
ValueCountFrequency (%) 
07< 0.1%
 
14< 0.1%
 
26< 0.1%
 
35733156.8%
 
49280.9%
 
524< 0.1%
 
61877918.6%
 
7580.1%
 
864426.4%
 
917341.7%
 
ValueCountFrequency (%) 
2517681.8%
 
2420< 0.1%
 
231050.1%
 
221980.2%
 
2114< 0.1%
 
201400.1%
 
192030.2%
 
181500.1%
 
172190.2%
 
1652635.2%
 

RESTRAINT_SYSTEM-USE
Real number (ℝ≥0)

Distinct count12
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.400394184296014
Minimum0
Maximum11
Zeros60
Zeros (%)0.1%
Memory size788.9 KiB
2020-08-25T01:23:02.019084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median7
Q37
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.900097461
Coefficient of variation (CV)0.2968719436
Kurtosis1.432523386
Mean6.400394184
Median Absolute Deviation (MAD)2
Skewness0.7345974513
Sum646235
Variance3.610370362
2020-08-25T01:23:02.131013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
74146241.1%
 
54076340.4%
 
1190178.9%
 
829732.9%
 
425232.5%
 
618981.9%
 
115421.5%
 
104700.5%
 
91310.1%
 
2730.1%
 
0600.1%
 
3560.1%
 
ValueCountFrequency (%) 
0600.1%
 
115421.5%
 
2730.1%
 
3560.1%
 
425232.5%
 
54076340.4%
 
618981.9%
 
74146241.1%
 
829732.9%
 
91310.1%
 
ValueCountFrequency (%) 
1190178.9%
 
104700.5%
 
91310.1%
 
829732.9%
 
74146241.1%
 
618981.9%
 
54076340.4%
 
425232.5%
 
3560.1%
 
2730.1%
 

SEX
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size788.9 KiB
1
65740
0
33573
2
 
1655
ValueCountFrequency (%) 
16574065.1%
 
03357333.3%
 
216551.6%
 
2020-08-25T01:23:02.888957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
16574065.1%
 
03357333.3%
 
216551.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number100968100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
16574065.1%
 
03357333.3%
 
216551.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common100968100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
16574065.1%
 
03357333.3%
 
216551.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII100968100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
16574065.1%
 
03357333.3%
 
216551.6%
 
Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size788.9 KiB
2
52355
0
46699
1
 
1914
ValueCountFrequency (%) 
25235551.9%
 
04669946.3%
 
119141.9%
 
2020-08-25T01:23:03.471371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
25235551.9%
 
04669946.3%
 
119141.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number100968100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
25235551.9%
 
04669946.3%
 
119141.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common100968100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
25235551.9%
 
04669946.3%
 
119141.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII100968100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
25235551.9%
 
04669946.3%
 
119141.9%
 

PERSON_TYPE
Real number (ℝ≥0)

Distinct count10
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.155831550590286
Minimum0
Maximum9
Zeros744
Zeros (%)0.7%
Memory size788.9 KiB
2020-08-25T01:23:03.579060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q36
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.551538049
Coefficient of variation (CV)0.808515286
Kurtosis-1.826692742
Mean3.155831551
Median Absolute Deviation (MAD)0
Skewness0.3444991946
Sum318638
Variance6.510346414
2020-08-25T01:23:03.689529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
15748056.9%
 
63681236.5%
 
753315.3%
 
07440.7%
 
82340.2%
 
22250.2%
 
51030.1%
 
334< 0.1%
 
43< 0.1%
 
92< 0.1%
 
ValueCountFrequency (%) 
07440.7%
 
15748056.9%
 
22250.2%
 
334< 0.1%
 
43< 0.1%
 
51030.1%
 
63681236.5%
 
753315.3%
 
82340.2%
 
92< 0.1%
 
ValueCountFrequency (%) 
92< 0.1%
 
82340.2%
 
753315.3%
 
63681236.5%
 
51030.1%
 
43< 0.1%
 
334< 0.1%
 
22250.2%
 
15748056.9%
 
07440.7%
 

target
Real number (ℝ≥0)

Distinct count8
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7829213216068456
Minimum0
Maximum7
Zeros9
Zeros (%)< 0.1%
Memory size788.9 KiB
2020-08-25T01:23:03.802116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.853606206
Coefficient of variation (CV)0.666064898
Kurtosis-1.300771467
Mean2.782921322
Median Absolute Deviation (MAD)1
Skewness0.4629695938
Sum280986
Variance3.435855968
2020-08-25T01:23:03.928124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
14211641.7%
 
42000719.8%
 
21507214.9%
 
51389013.8%
 
686748.6%
 
79010.9%
 
32990.3%
 
09< 0.1%
 
ValueCountFrequency (%) 
09< 0.1%
 
14211641.7%
 
21507214.9%
 
32990.3%
 
42000719.8%
 
51389013.8%
 
686748.6%
 
79010.9%
 
ValueCountFrequency (%) 
79010.9%
 
686748.6%
 
51389013.8%
 
42000719.8%
 
32990.3%
 
21507214.9%
 
14211641.7%
 
09< 0.1%
 

Interactions

2020-08-25T01:22:18.969981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:19.169035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:19.361171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:19.550324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:19.732948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:19.923607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:20.291792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:20.477312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:20.682783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:20.869561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:21.056270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:21.244641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:21.428834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:21.807998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:22.016799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:22.198844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:22.373322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:22.540014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:22.716912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:23.073010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:23.243217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:23.422624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:23.597196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:23.772939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:23.958917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:24.316707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:24.670760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:24.854601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:25.027070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:25.201781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:25.366500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:25.541094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:25.882282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:26.051399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:26.220361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:26.390872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:26.570966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:26.741523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:26.912549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:27.259086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:27.431758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:27.604734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:27.765221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:27.918137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:28.081682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:28.398632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:28.558346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:28.723487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:28.890615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:29.054891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:29.218346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:29.374301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:29.913021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:30.105578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:30.282593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:30.456173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:30.625692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:30.801046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:31.147168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:31.318328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:31.496326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:31.674559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:31.847207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:32.020613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:32.187387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:32.563784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:32.741611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:32.916725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:33.090481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:33.260310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:33.441564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:33.759160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:33.933706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:34.101681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:34.260341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:34.422390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:34.584569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:34.740289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:37.763896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:37.938792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:38.103954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:38.267257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:38.420995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:38.583790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:38.916043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:39.073748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:39.242898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:39.400718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:39.559961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:39.723637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:39.892630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:40.240203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:40.419643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:40.593936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:40.976013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:41.142968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:41.327258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:41.660547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:41.829161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:42.001035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:42.166172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:42.337211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:42.506227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:42.669177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:43.008177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:43.180814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:43.346889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:43.508078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:43.664596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:43.846973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:44.172753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:44.333168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:44.499075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:44.657578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:44.822227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:44.988884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:45.143913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:45.482294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:45.660709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:45.832546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:46.000067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:46.351105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:46.522889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:46.846648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:47.007656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:47.176176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:47.340082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:47.505527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:47.669513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:47.832952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:48.163372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:48.340369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:48.504091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:48.665898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:48.827924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:49.000428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:49.332583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:49.493512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:49.660569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:49.822866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:49.989198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:50.153079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:50.315083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:50.654833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:50.828619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:50.994755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:51.158115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:51.314334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:51.490831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:52.016868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:52.178159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:52.344260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:52.503884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:52.665169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:52.833506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:53.013339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:53.365457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:53.557504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:53.741985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:53.932419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:54.106377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:54.289986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:54.647996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:54.822082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:55.007188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:55.184801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:55.364788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:55.545532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:55.720476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:23:04.085838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:23:04.477560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:23:04.866491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:23:05.241528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-25T01:23:05.563524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-25T01:22:56.073494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:22:56.733683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

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Last rows

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Duplicate rows

Most frequent

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